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Register a new userConnection between forest fire emission and COVID-19 incidents in West Coast regions of the United States
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Added by
Srikanta Sannigrahi
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
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Forest fires impact on soil, water and biota resources has been widely researched. Although forest fires profoundly impact the atmosphere and air quality across the ecosystems, much less research has been developed to examine its impact on the current pandemic. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID 19) casualties. The spatiotemporal concentrations of particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) were collected from August 1 to October 30 for 2020 (fire year) and 2019 (reference year). Both spatial (Multiscale Geographically Weighted Regression) and non spatial (negative binomial regression) regression analysis was performed to assess the adverse effects of fire emission on human health. The in situ data led measurements showed that the maximum increases in PM2.5, PM10, and NO2 concentrations were clustered in the West Coastal fire-prone states during the August 1 to October 30 period. The average concentration of particulate matter (PM2.5 and PM10) and NO2 were increased in all the fire states affected badly by forest fires. The average PM2.5 concentration over the period was recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, which was increased up to 24.9, 13.4, 25, and 17 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID 19 incidents. A total of 30 models were developed for analyzing the spatial non-stationary and local association between the predictor and response factors. All these spatial models have demonstrated a statistically significant association between fire emissions and COVID counts. More thorough research is needed to better understand the complex association between forest fire and human health.
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Forest fires have a profound impact on the atmospheric environment and air quality across the ecosystems. The recent west coast forest fire in the United States of America (USA) has broken all the past records and caused severe environmental and public health burdens. As of middle September, nearly 6 million acres forest area were burned, and more than 25 casualties were reported so far. In this study, both satellite and in-situ air pollution data were utilized to examine the effects of this unprecedented wildfire on the atmospheric environment. The spatiotemporal concentrations of total six air pollutants, i.e. carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), particulate matter (PM2.5 and PM10), and aerosol index (AI), were measured for the periods of 15 August to 15 September for 2020 (fire year) and 2019 (reference year). The in-situ data-led measurements show that the highest increases in CO (ppm), PM2.5, and PM10 concentrations ({mu}g/m3) were clustered around the west coastal fire-prone states, during the 15 August - 15 September period. The average CO concentration (ppm) was increased most significantly in Oregon (1147.10), followed by Washington (812.76), and California (13.17). Meanwhile, the concentration ({mu}g/m3) in particulate matter (both PM2.5 and PM10), was increased in all three states affected severely by wildfires. Changes (positive) in both PM2.5 and PM10 were measured highest in Washington (45.83 and 88.47 for PM2.5 and PM10), followed by Oregon (41.99 and 62.75 for PM2.5 and PM10), and California (31.27 and 35.04 for PM2.5 and PM10). The average level of exposure to CO, PM2.5, and PM10 was also measured for all the three fire-prone states. The results of the exposure assessment revealed a strong tradeoff association between wildland fire and local/regional air quality standard.
Since December 2019, the world has been witnessing the gigantic effect of an unprecedented global pandemic called Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) - COVID-19. So far, 38,619,674 confirmed cases and 1,093,522 confirmed deaths due to COVID-19 have been reported. In the United States (US), the cases and deaths are recorded as 7,833,851 and 215,199. Several timely researches have discussed the local and global effects of the confounding factors on COVID-19 casualties in the US. However, most of these studies considered little about the time varying associations between and among these factors, which are crucial for understanding the outbreak of the present pandemic. Therefore, this study adopts various relevant approaches, including local and global spatial regression models and machine learning to explore the causal effects of the confounding factors on COVID-19 counts in the contiguous US. Totally five spatial regression models, spatial lag model (SLM), ordinary least square (OLS), spatial error model (SEM), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR), are performed at the county scale to take into account the scale effects on modelling. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain the maximum model variances. For COVID-19 deaths, both (domestic and international) migration and income factors play a crucial role in explaining spatial differences of COVID-19 death counts across counties. The local coefficient of determination (R2) values derived from the GWR and MGWR models are found very high over the Wisconsin-Indiana-Michigan (the Great Lake) region, as well as several parts of Texas, California, Mississippi and Arkansas.
Governments issue stay at home orders to reduce the spread of contagious diseases, but the magnitude of such orders effectiveness is uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies effect with greater accuracy. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States. We estimate the effect of stay-at-home orders using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) reduction in weekly cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities where they were implemented.
The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information. Version 1.0 of the survey contains 8,723 responses that are publicly available. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. This article details the methodology adopted for the collection, cleaning, and processing of the data. In addition, the data are weighted to be representative of national and regional demographics. This survey dataset can aid researchers, policymakers, businesses, and government agencies in understanding both the extent of behavioral shifts and the likelihood that these changes will persist after COVID-19.
The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.
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